1,303 research outputs found
Learn Convolutional Neural Network for Face Anti-Spoofing
Though having achieved some progresses, the hand-crafted texture features,
e.g., LBP [23], LBP-TOP [11] are still unable to capture the most
discriminative cues between genuine and fake faces. In this paper, instead of
designing feature by ourselves, we rely on the deep convolutional neural
network (CNN) to learn features of high discriminative ability in a supervised
manner. Combined with some data pre-processing, the face anti-spoofing
performance improves drastically. In the experiments, over 70% relative
decrease of Half Total Error Rate (HTER) is achieved on two challenging
datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art.
Meanwhile, the experimental results from inter-tests between two datasets
indicates CNN can obtain features with better generalization ability. Moreover,
the nets trained using combined data from two datasets have less biases between
two datasets.Comment: 8 pages, 9 figures, 7 table
On the Learning of Deep Local Features for Robust Face Spoofing Detection
Biometrics emerged as a robust solution for security systems. However, given
the dissemination of biometric applications, criminals are developing
techniques to circumvent them by simulating physical or behavioral traits of
legal users (spoofing attacks). Despite face being a promising characteristic
due to its universality, acceptability and presence of cameras almost
everywhere, face recognition systems are extremely vulnerable to such frauds
since they can be easily fooled with common printed facial photographs.
State-of-the-art approaches, based on Convolutional Neural Networks (CNNs),
present good results in face spoofing detection. However, these methods do not
consider the importance of learning deep local features from each facial
region, even though it is known from face recognition that each facial region
presents different visual aspects, which can also be exploited for face
spoofing detection. In this work we propose a novel CNN architecture trained in
two steps for such task. Initially, each part of the neural network learns
features from a given facial region. Afterwards, the whole model is fine-tuned
on the whole facial images. Results show that such pre-training step allows the
CNN to learn different local spoofing cues, improving the performance and the
convergence speed of the final model, outperforming the state-of-the-art
approaches
How far did we get in face spoofing detection?
The growing use of control access systems based on face recognition shed
light over the need for even more accurate systems to detect face spoofing
attacks. In this paper, an extensive analysis on face spoofing detection works
published in the last decade is presented. The analyzed works are categorized
by their fundamental parts, i.e., descriptors and classifiers. This structured
survey also brings the temporal evolution of the face spoofing detection field,
as well as a comparative analysis of the works considering the most important
public data sets in the field. The methodology followed in this work is
particularly relevant to observe trends in the existing approaches, to discuss
still opened issues, and to propose new perspectives for the future of face
spoofing detection
Face Spoofing Detection by Fusing Binocular Depth and Spatial Pyramid Coding Micro-Texture Features
Robust features are of vital importance to face spoofing detection, because
various situations make feature space extremely complicated to partition. Thus
in this paper, two novel and robust features for anti-spoofing are proposed.
The first one is a binocular camera based depth feature called Template Face
Matched Binocular Depth (TFBD) feature. The second one is a high-level
micro-texture based feature called Spatial Pyramid Coding Micro-Texture (SPMT)
feature. Novel template face registration algorithm and spatial pyramid coding
algorithm are also introduced along with the two novel features. Multi-modal
face spoofing detection is implemented based on these two robust features.
Experiments are conducted on a widely used dataset and a comprehensive dataset
constructed by ourselves. The results reveal that face spoofing detection with
the fusion of our proposed features is of strong robustness and time
efficiency, meanwhile outperforming other state-of-the-art traditional methods.Comment: 5 pages, 2 figures, accepted by 2017 IEEE International Conference on
Image Processing (ICIP
Face De-Spoofing: Anti-Spoofing via Noise Modeling
Many prior face anti-spoofing works develop discriminative models for
recognizing the subtle differences between live and spoof faces. Those
approaches often regard the image as an indivisible unit, and process it
holistically, without explicit modeling of the spoofing process. In this work,
motivated by the noise modeling and denoising algorithms, we identify a new
problem of face de-spoofing, for the purpose of anti-spoofing: inversely
decomposing a spoof face into a spoof noise and a live face, and then utilizing
the spoof noise for classification. A CNN architecture with proper constraints
and supervisions is proposed to overcome the problem of having no ground truth
for the decomposition. We evaluate the proposed method on multiple face
anti-spoofing databases. The results show promising improvements due to our
spoof noise modeling. Moreover, the estimated spoof noise provides a
visualization which helps to understand the added spoof noise by each spoof
medium.Comment: To appear in ECCV 2018. The first two authors contributed equally to
this wor
Discriminative Representation Combinations for Accurate Face Spoofing Detection
Three discriminative representations for face presentation attack detection
are introduced in this paper. Firstly we design a descriptor called spatial
pyramid coding micro-texture (SPMT) feature to characterize local appearance
information. Secondly we utilize the SSD, which is a deep learning framework
for detection, to excavate context cues and conduct end-to-end face
presentation attack detection. Finally we design a descriptor called template
face matched binocular depth (TFBD) feature to characterize stereo structures
of real and fake faces. For accurate presentation attack detection, we also
design two kinds of representation combinations. Firstly, we propose a
decision-level cascade strategy to combine SPMT with SSD. Secondly, we use a
simple score fusion strategy to combine face structure cues (TFBD) with local
micro-texture features (SPMT). To demonstrate the effectiveness of our design,
we evaluate the representation combination of SPMT and SSD on three public
datasets, which outperforms all other state-of-the-art methods. In addition, we
evaluate the representation combination of SPMT and TFBD on our dataset and
excellent performance is also achieved.Comment: To be published in Pattern Recognitio
An Overview of Face Liveness Detection
Face recognition is a widely used biometric approach. Face recognition
technology has developed rapidly in recent years and it is more direct, user
friendly and convenient compared to other methods. But face recognition systems
are vulnerable to spoof attacks made by non-real faces. It is an easy way to
spoof face recognition systems by facial pictures such as portrait photographs.
A secure system needs Liveness detection in order to guard against such
spoofing. In this work, face liveness detection approaches are categorized
based on the various types techniques used for liveness detection. This
categorization helps understanding different spoof attacks scenarios and their
relation to the developed solutions. A review of the latest works regarding
face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness
detection approach.Comment: International Journal on Information Theory (IJIT), Vol.3, No.2,
April 201
Learning Generalized Spoof Cues for Face Anti-spoofing
Many existing face anti-spoofing (FAS) methods focus on modeling the decision
boundaries for some predefined spoof types. However, the diversity of the spoof
samples including the unknown ones hinders the effective decision boundary
modeling and leads to weak generalization capability. In this paper, we
reformulate FAS in an anomaly detection perspective and propose a
residual-learning framework to learn the discriminative live-spoof differences
which are defined as the spoof cues. The proposed framework consists of a spoof
cue generator and an auxiliary classifier. The generator minimizes the spoof
cues of live samples while imposes no explicit constraint on those of spoof
samples to generalize well to unseen attacks. In this way, anomaly detection is
implicitly used to guide spoof cue generation, leading to discriminative
feature learning. The auxiliary classifier serves as a spoof cue amplifier and
makes the spoof cues more discriminative. We conduct extensive experiments and
the experimental results show the proposed method consistently outperforms the
state-of-the-art methods. The code will be publicly available at
https://github.com/vis-var/lgsc-for-fas.Comment: 16 page
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Face Anti-spoofing gains increased attentions recently in both academic and
industrial fields. With the emergence of various CNN based solutions, the
multi-modal(RGB, depth and IR) methods based CNN showed better performance than
single modal classifiers. However, there is a need for improving the
performance and reducing the complexity. Therefore, an extreme light network
architecture(FeatherNet A/B) is proposed with a streaming module which fixes
the weakness of Global Average Pooling and uses less parameters. Our single
FeatherNet trained by depth image only, provides a higher baseline with 0.00168
ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure
with ``ensemble + cascade'' structure is presented to satisfy the performance
preferred use cases. Meanwhile, the MMFD dataset is collected to provide more
attacks and diversity to gain better generalization. We use the fusion method
in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the
result of 0.0013(ACER), 0.999(TPR@FPR=10e-2), 0.998(TPR@FPR=10e-3) and
0.9814(TPR@FPR=10e-4).Comment: 10 pages;6 figure
Improving Face Anti-Spoofing by 3D Virtual Synthesis
Face anti-spoofing is crucial for the security of face recognition systems.
Learning based methods especially deep learning based methods need large-scale
training samples to reduce overfitting. However, acquiring spoof data is very
expensive since the live faces should be re-printed and re-captured in many
views. In this paper, we present a method to synthesize virtual spoof data in
3D space to alleviate this problem. Specifically, we consider a printed photo
as a flat surface and mesh it into a 3D object, which is then randomly bent and
rotated in 3D space. Afterward, the transformed 3D photo is rendered through
perspective projection as a virtual sample. The synthetic virtual samples can
significantly boost the anti-spoofing performance when combined with a proposed
data balancing strategy. Our promising results open up new possibilities for
advancing face anti-spoofing using cheap and large-scale synthetic data.Comment: Accepted to ICB 201
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